Among the wealth of handsets, connected cars and wireless antennas on display, one of the hot topics at this year’s Mobile World Congress in Barcelona was edge computing.
With cloud computing power and data processing moved to the edge of the network, closer to where the data itself is produced, edge computing removes the need for traffic to be backhauled to and from a remote data centre. Not only does this mean that mobile devices will soon be faster and lighter, having less need for internal compute resources but, more importantly, it will underpin the potential of 5G and unlock the opportunities that its high speed and ultra-low latency is set to offer consumers and businesses alike.
The myriad applications and services of the burgeoning Internet of Things (IoT), for example, such as vehicle-to-vehicle communications, automated assembly lines, and arrays of sensors that make up a smart city, will all benefit from using 5G technology to support the connectivity upon which they depend. And with a growing number of IoT applications in fields such as transport, agriculture and aviation joining more consumer-focused applications such as smart TVs, wearables, and smart heating and lighting, it’s more important than ever for operators to have assurance their connectivity remains ubiquitous, consistent and reliable.
It’s little surprise then, that edge computing is gaining traction, as evidenced by AT&T’s and VMware’s announcements at Mobile World Congress that they were moving into the edge computing space, just weeks after chip manufacturing giants Qualcomm Technologies and Intel launched their own offerings.
There will, of course, be a new set of challenges to face, however, and complete visibility across the entire IoT lifecycle will be required if operators are to address these.
Organisations are increasingly virtualising network components and functions in a bid to increase speed and agility while saving on costs. To better support the increase in their customer’s data traffic, they’re leveraging new NFV architecture to deploy a range of SD-WAN (software-defined WAN) and C-RAN (cloud-based RAN) solutions. At the end of 2016, for example, South Korea’s SK Telecom partnered with Nokia to become the first operator to commercially install cloud C-RAN at the end of 2016, while many global service providers including Vodafone, Verizon and BT have launched managed SD-WAN services to minimise customer concerns around infrastructure management.
The data generated by virtual solutions such as these will, if managed properly, provide operators with the intelligence they need for actionable, meaningful insights, that will help inform their network policy and traffic management systems. This flow of information, and the intelligence derived from it will, over time, lead to networks becoming automated and self-optimising, allowing operators to allocate capacity to areas where it’s most needed: to manage peaks in network demand or, in the case of IoT traffic, to manage demands of smart cities, self-driving cars, or automated manufacturing plants.
Such use cases are still largely hypothetical, however, and are likely to generate an unprecedented level of data traffic. Operators will therefore need full visibility to effectively manage their networks, particularly in the case of the IoT, where they will be required to report on the status of any mission-critical systems. Indeed, IoT technologies underpin a growing number of critical applications, so the need for assurance around security and service is of prime importance. It makes perfect sense for an operator to reconfigure a network to accommodate the increasing demands of 5G and the IoT, but they must be able to derive some meaningful insight from the data being produced; a lack of visibility into what’s occurring on the network could result in some very serious consequences.
A frustrated Netflix subscriber unable to watch the latest episode of Stranger Things can simply contact the customer service centre. Automated machines on a production line, however, can’t contact anyone to let them know they’re experiencing connectivity issues, potentially having a knock-on effect throughout a business’s supply chain.
Much of the huge volume of data generated by the billions of touchpoints in the IoT will be unstructured, delivered at high speed from a wide variety of disparate sources, and won’t translate into actionable insight even with the application of advanced analytics. If this data isn’t normalised and correlated in the context of service delivery, operations and business performance, then the quality of business insight and the value it delivers will be worthless.
Speed and performance benefits aside, the application of edge computing will only complicate this situation further, and traditional network monitoring and assurance tools may no longer be able to provide operators with the pane of glass they need into this new environment.
To gain the insight they need for full and effective infrastructure management, operators should employ a smart data solution that will maintain visibility throughout every aspect of the IoT lifecycle, from testing, through the monitoring and analysis of live traffic, to network orchestration and automation. With computing moving closer to the edge, smart data will soon become the oil that fuels a network’s engines.
Offering unlimited scale, across all aspects of the network, both physical and virtual, smart data is real-time, scalable, metadata imbued with user experience derived from network traffic. By accessing and analysing this data in real time, an operator will gain valuable new insights into how the connected IoT devices, services and applications on their network behave, how they interact with the network, and the type of traffic patterns they produce. Access to smart data will ultimately put operators in a position from which to make more informed decisions about how to optimise their networks, where to allocate capacity, and how to boost performance. What’s more, the visibility that smart data provides makes it possible for operators to identify anomalies within the network; significant changes that could indicate issues such as network congestion, which could have a potential impact on hundreds of thousands of connected devices.
For operators and their enterprise customers who rely on the network to provide a communication backbone to support their IoT deployments, it’s fair to say that actionable intelligence of this type is worth its weight in gold.
New, Faster, More Efficient
According to the GSMA, 77 operators are trialling are currently trialling 5G across 49 countries, with Verizon, China Mobile, Telefonica and Sprint, among others, discussing plans for rolling out the technology in select areas within a matter of months. 5G networks will soon be with us and, with GSMA Intelligence predicting that there will be 25 billion IoT connections across the world by 2025, the need to support the new data loads and traffic patterns they will both introduce is only set to increase, along with a growing demand for reduced latency and more efficient computing power.
As network infrastructure continues to be moved to the edge, harnessing NFV and cloud technology to enable the delivery of new, faster, more efficient services, there is a clear need for visibility across these new-look, complex networks if operators are to make the most of their new investment.
Employing a smart data solution will provide operators with the visibility and confidence they need to monitor the sheer breadth and depth of the IoT ecosystem, assured that everything is connected.